dc.creator | García Nieto, José Manuel | es |
dc.creator | Ferrer, Javier | es |
dc.creator | Alba, Enrique | es |
dc.date.accessioned | 2021-05-11T11:00:20Z | |
dc.date.available | 2021-05-11T11:00:20Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | García Nieto, J.M., Ferrer, J. y Alba, E. (2014). Optimising Traffic Lights with Metaheuristics: Reduction of Car Emissions and Consumption. En IJCNN 2014: International Joint Conference on Neural Networks (48-54), Beijing, China: IEEE Computer Society. | |
dc.identifier.isbn | 978-1-4799-1484-5 | es |
dc.identifier.issn | 2161-4393 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108854 | |
dc.description.abstract | In last years, enhancing the vehicular traffic flow
becomes a mandatory task to minimize the impact of polluting
emissions and unsustainable fuel consumption in our cities. Smart
Mobility optimisation emerges then, with the goal of improving
the traffic management in the city. With this aim, we propose in
this paper an optimisation strategy based on swarm intelligence
to find efficient cycle programs for traffic lights deployed in large
urban areas. In concrete, in this work we focus on the improvement
of the traffic flow with the global purpose of reducing
contaminant emissions (CO2 and NOx) and fuel consumption
in the analyzed areas. For the sake of standardization, we follow
European Union reference framework for traffic emissions, called
HandBook Emission FActors (HBEFA). As a case study, we
have concentrated in two extensive urban areas in the cities
of Malaga and Seville (in Spain). After several comparisons
between different optimisation techniques (Differential Evolution
and Random Search), as well as other solutions provided by
experts, our proposal is shown to obtain significant reductions of
fuel consumption and gas emissions. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2011-28194 | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad BES-2012-055967 | es |
dc.description.sponsorship | VSB-Tech. Univ. of Ostrava and Universidad de Málaga, Andalucía Tech 8.06/5.47.4142 | es |
dc.format | application/pdf | es |
dc.format.extent | 7 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IJCNN 2014: International Joint Conference on Neural Networks (2014), pp. 48-54. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Optimising Traffic Lights with Metaheuristics: Reduction of Car Emissions and Consumption | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | TIN2011-28194 | es |
dc.relation.projectID | BES-2012-055967 | es |
dc.relation.projectID | 8.06/5.47.4142 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/6889749 | es |
dc.identifier.doi | 10.1109/IJCNN.2014.6889749 | es |
dc.publication.initialPage | 48 | es |
dc.publication.endPage | 54 | es |
dc.eventtitle | IJCNN 2014: International Joint Conference on Neural Networks | es |
dc.eventinstitution | Beijing, China | es |
dc.relation.publicationplace | New York, USA | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |
dc.contributor.funder | University of Ostrava | es |
dc.contributor.funder | Universidad de Málaga | es |